Publication Type

Conference Proceeding Article

Publication Date

12-2014

Abstract

A document network refers to a data type that can be represented as a graph of vertices, where each vertex is associated with a text document. Examples of such a data type include hyperlinked Web pages, academic publications with citations, and user profiles in social networks. Such data have very high-dimensional representations, in terms of text as well as network connectivity. In this paper, we study the problem of embedding, or finding a low-dimensional representation of a document network that "preserves" the data as much as possible. These embedded representations are useful for various applications driven by dimensionality reduction, such as visualization or feature selection. While previous works in embedding have mostly focused on either the textual aspect or the network aspect, we advocate a holistic approach by finding a unified low-rank representation for both aspects. Moreover, to lend semantic interpretability to the low-rank representation, we further propose to integrate topic modeling and embedding within a joint model. The gist is to join the various representations of a document (words, links, topics, and coordinates) within a generative model, and to estimate the hidden representations through MAP estimation. We validate our model on real-life document networks, showing that it outperforms comparable baselines comprehensively on objective evaluation metrics.

Keywords

dimensionality reduction, document network, embedding, visualization, topic modeling, generative model

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

2014 IEEE International Conference on Data Mining ICDM: Shenzhen, China, 14-17 December: Proceedings

First Page

270

Last Page

279

ISBN

9781479943036

Identifier

10.1109/ICDM.2014.119

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1109/ICDM.2014.119

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